Share：
Share this content in WeChat
X
[Chinese] [PDF] 41 1
• Reviews •
Advances in the application of MRI for evaluating therapeutic efficacy of major treatment modalities in lung cancer
QIN Chongping  DANG Shan  YU Nan 

Cite this article as QIN C P, DANG S, YU N. Advances in the application of MRI for evaluating therapeutic efficacy of major treatment modalities in lung cancer[J]. Chin J Magn Reson Imaging, 2026, 17(8): 190-197. DOI:10.12015/issn.1674-8034.2026.08.022.


[Abstract] Lung cancer is a malignant tumor that poses a serious threat to human health. With the development of precision medicine and the expanding use of immunotherapy, targeted therapy, radiotherapy and chemotherapy, evaluation of therapeutic efficacy has become a key component of individualized treatment for lung cancer. Magnetic resonance imaging (MRI), which does not involve ionizing radiation and offers multiparametric and multidimensional imaging capabilities, can dynamically reflect tumor responses to treatment in terms of perfusion, diffusion, microstructure and functional status, providing a sound basis for its application in evaluating therapeutic efficacy in lung cancer. Existing studies have mainly focused on a single treatment modality, MRI technique or evaluation scenario, while MRI parameters, clinical value and limitations across different treatment modalities have not yet been comprehensively summarized. Focusing on four major treatment modalities, immunotherapy, targeted therapy, radiotherapy and chemotherapy, this review summarizes advances in the application of functional MRI and related emerging techniques for evaluating therapeutic efficacy in lung cancer. It specifically summarizes the value of these techniques in benefit stratification, response monitoring and differentiation of pseudoprogression during immunotherapy; efficacy evaluation and resistance monitoring during targeted therapy; radiotherapy guidance and preservation of pulmonary function; and chemosensitivity assessment and differentiation of complex lesions during chemotherapy. This review also analyzes current limitations, including insufficient standardization, small sample sizes and poor reproducibility, and proposes future directions focusing on standardized workflows, multicenter prospective validation and multimodal information integration, with the aim of providing a reference for the standardized application of MRI in evaluating therapeutic efficacy in lung cancer.
[Keywords] lung cancer；magnetic resonance imaging；evaluation of efficacy；individualized treatment；immunotherapy；targeted therapy；radiotherapy；chemotherapy

QIN Chongping1   DANG Shan2   YU Nan1, 2*  

1 College of Medical Technology, Shaanxi University of Traditional Chinese Medicine, Xianyang 712046, China

2 Department of Medical Imaging, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang 712000, China

Corresponding author: YU N, E-mail: yunan0512@sina.com

Conflicts of interest   None.

Received  2026-03-25
Accepted  2026-07-31
DOI: 10.12015/issn.1674-8034.2026.08.022
Cite this article as QIN C P, DANG S, YU N. Advances in the application of MRI for evaluating therapeutic efficacy of major treatment modalities in lung cancer[J]. Chin J Magn Reson Imaging, 2026, 17(8): 190-197. DOI:10.12015/issn.1674-8034.2026.08.022.

[1]
BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[2]
OWENS C, HINDOCHA S, LEE R, et al. The lung cancers: staging and response, CT, 18F-FDG PET/CT, MRI, DWI: review and new perspectives[J/OL]. Br J Radiol, 2023, 96(1148): 20220339 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/37097296/. DOI: 10.1259/bjr.20220339.
[3]
ESSA N BIN, KAPLAR Z, BALAJI N, et al. PET/CT in treatment response assessment in lung cancer. When should it be recommended[J]. Nucl Med Commun, 2023, 44(12): 1059-1066. DOI: 10.1097/mnm.0000000000001757.
[4]
LI Y, LIANG F, XIAO S J, et al. A narrative review of pathologic response in non-small cell lung cancer: challenges, implications, and future directions[J]. Transl Lung Cancer Res, 2025, 14(11): 5118-5136. DOI: 10.21037/tlcr-2025-584.
[5]
FRANZI S, SERESINI G, BORELLA P, et al. Liquid biopsy in non-small cell lung cancer: a meta-analysis of state-of-the-art and future perspectives[J/OL]. Front Genet, 2023, 14: 1254839 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/38116291/. DOI: 10.3389/fgene.2023.1254839.
[6]
SALEHJAHROMI M, KARPINETS T V, SUJIT S J, et al. Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept[J/OL]. Cell Rep Med, 2024, 5(3): 101463 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/38471502/. DOI: 10.1016/j.xcrm.2024.101463.
[7]
MLADIN R, OANCEA C, STOICESCU E R, et al. The role of 18F-FDG PET/CT in monitoring immunotherapy response in non-small cell lung cancer: current evidence and challenges: a narrative review[J/OL]. Diagnostics (Basel), 2025, 15(21): 2754 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/41226048/. DOI: 10.3390/diagnostics15212754.
[8]
OHNO Y, OZAWA Y, KOYAMA H, et al. State of the art MR imaging for lung cancer TNM stage evaluation[J/OL]. Cancers, 2023, 15(3): 950 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/36765907/. DOI: 10.3390/cancers15030950.
[9]
TÁRNOKI D L, KARLINGER K, RIDGE C A, et al. Lung imaging methods: indications, strengths and limitations[J/OL]. Breathe, 2024, 20(3): 230127 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/39360028/. DOI: 10.1183/20734735.0127-2023.
[10]
MA C, WANG X, NIE K, et al. Recent technical advancements and clinical applications of MR-guided radiotherapy in lung cancer treatment[J/OL]. Front Oncol, 2025, 15: 1622060 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/40666105/. DOI: 10.3389/fonc.2025.1622060.
[11]
JAGODA P, FLECKENSTEIN J, SONNHOFF M, et al. Diffusion-weighted MRI improves response assessment after definitive radiotherapy in patients with NSCLC[J/OL]. Cancer Imaging, 2021, 21(1): 15 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/33478592/. DOI: 10.1186/s40644-021-00384-9.
[12]
GRUNDBERG O, SKRIBEK M, SWERKERSSON S, et al. Diffusion weighted MRI and apparent diffusion coefficient as a prognostic biomarker in evaluating chemotherapy-antiangiogenic treated stage IV non-small cell lung cancer: a prospective, single-arm, open-label, clinical trial (BevMar)[J/OL]. Eur J Radiol, 2024, 177: 111557 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/38954912/. DOI: 10.1016/j.ejrad.2024.111557.
[13]
MESSANA G, BORTOLOTTO C, THULASI SEETHA S, et al. Non-invasive PD-L1 stratification in non-small cell lung cancer using dynamic contrast-enhanced MRI[J]. Eur Radiol, 2025, 35(9): 5569-5580. DOI: 10.1007/s00330-025-11524-1.
[14]
YANG C, ZHU F D, LI Q L, et al. Evaluation of PD-L1 expression in patients with non-small cell lung cancer using DCE-MRI quantitative analysis[J/OL]. Clin Respir J, 2025, 19(10): e70125 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/41014107/. DOI: 10.1111/crj.70125.
[15]
BORTOLOTTO C, STELLA G M, MESSANA G, et al. Correlation between PD-L1 expression of non-small cell lung cancer and data from IVIM-DWI acquired during magnetic resonance of the Thorax: preliminary results[J/OL]. Cancers, 2022, 14(22): 5634 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/36428726/. DOI: 10.3390/cancers14225634.
[16]
MENG N, FU F F, SUN J, et al. Sensitivity and specificity of amide proton transfer-weighted imaging for assessing programmed death-ligand 1 status in non-small cell lung cancer: a comparative study with intravoxel incoherent motion and 18F-FDG PET[J]. Quant Imaging Med Surg, 2022, 12(9): 4474-4487. DOI: 10.21037/qims-22-189.
[17]
BAO X, BIAN D L, YANG X, et al. Multiparametric MRI for evaluation of pathological response to the neoadjuvant chemo-immunotherapy in resectable non-small-cell lung cancer[J]. Eur Radiol, 2023, 33(12): 9182-9193. DOI: 10.1007/s00330-023-09813-8.
[18]
ZHENG Y, ZHOU L, HUANG W J, et al. Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy[J/OL]. Cancer Imaging, 2024, 24(1): 71 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/38863062/. DOI: 10.1186/s40644-024-00713-8.
[19]
HAN Q J, LI S L, XIANG Z M. The value of T1 mapping and intravoxel incoherent motion parameters in predicting PD-L1 expression and dynamically monitoring immunotherapy in advanced lung cancer[J/OL]. Front Oncol, 2026, 16: 1687192 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/41768249/. DOI: 10.3389/fonc.2026.1687192.
[20]
KARAYAMA M, YOSHIZAWA N, SUGIYAMA M, et al. Intravoxel incoherent motion magnetic resonance imaging for predicting the long-term efficacy of immune checkpoint inhibitors in patients with non-small-cell lung cancer[J/OL]. Lung Cancer, 2020, 143: 47-54 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/32203770/. DOI: 10.1016/j.lungcan.2020.03.013.
[21]
WANG D Q, LIU S R, FU J, et al. Correlation of Ktrans derived from dynamic contrast-enhanced MRI with treatment response and survival in locally advanced NSCLC patients undergoing induction immunochemotherapy and concurrent chemoradiotherapy[J/OL]. J Immunother Cancer, 2024, 12(6): e008574 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/38910009/. DOI: 10.1136/jitc-2023-008574.
[22]
PERSIGEHL T, LENNARTZ S, SCHWARTZ L H. iRECIST: how to do it[J/OL]. Cancer Imaging, 2020, 20(1): 2 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/31900236/. DOI: 10.1186/s40644-019-0281-x.
[23]
EISENHAUER E A, THERASSE P, BOGAERTS J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)[J]. Eur J Cancer, 2009, 45(2): 228-247. DOI: 10.1016/j.ejca.2008.10.026.
[24]
SEYMOUR L, BOGAERTS J, PERRONE A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics[J/OL]. Lancet Oncol, 2017, 18(3): e143-e152 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/28271869/. DOI: 10.1016/S1470-2045(17)30074-8.
[25]
KIM J H, LEE J W, PARK K, et al. Dynamic contrast-enhanced MRI for response evaluation of non-small cell lung cancer in therapy with epidermal growth factor receptor tyrosine kinase inhibitors: a pilot study[J]. Ann Palliat Med, 2021, 10(2): 1589-1598. DOI: 10.21037/apm-19-622.
[26]
HUANG Y S, CHEN J L, CHEN H M, et al. Assessing tumor angiogenesis using dynamic contrast-enhanced integrated magnetic resonance-positron emission tomography in patients with non-small-cell lung cancer[J/OL]. BMC Cancer, 2021, 21(1): 348 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/33794813/. DOI: 10.1186/s12885-021-08064-4.
[27]
ZOU M Y, ZHANG B Q, SHI L, et al. Correlation of MRI quantitative perfusion parameters with EGFR VEGF and EGFR gene mutations in non-small cell cancer[J/OL]. Sci Rep, 2024, 14(1): 4447 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/38396128/. DOI: 10.1038/s41598-024-55033-5.
[28]
WANG Y B, HU H, YIN Y D, et al. Prediction of EGFR mutation status in non-small cell lung cancer based on multiparametric MRI radiomics[J/OL]. BMC Med Imaging, 2025, 25(1): 503 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/41387785/. DOI: 10.1186/s12880-025-02029-w.
[29]
LV X N, LI Y, WANG B, et al. Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases[J]. Quant Imaging Med Surg, 2023, 13(12): 8599-8610. DOI: 10.21037/qims-23-822.
[30]
FAN Y, HE L Z, YANG H Z, et al. Preoperative MRI-based radiomics of brain metastasis to assess T790M resistance mutation after EGFR-TKI treatment in NSCLC[J]. J Magn Reson Imaging, 2023, 57(6): 1778-1787. DOI: 10.1002/jmri.28441.
[31]
LI Y L, LI Z, WANG H T, et al. Visualizing the vascular-cellular microenvironment in lung cancer brain metastasis via multiparametric fusion of DCE-MRI and DWI[J/OL]. Front Oncol, 2025, 15: 1645589 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/41229500/. DOI: 10.3389/fonc.2025.1645589.
[32]
YE C Y, LIN Q B, JIN Z, et al. Predictive effect of DCE-MRI and DWI in brain metastases from NSCLC[J]. Open Med (Wars), 2021, 16(1): 1265-1275. DOI: 10.1515/med-2021-0260.
[33]
Lung Cancer Medical Education Committee of Chinese Medical Education Association, Metastasis Lung Cancer Collaboration Group, Youth Specialists Committee of Beijing Medical Reward Foundation, ZHAO J, et al. Chinese expert consensus on clinical management of oncogene addicted non-small cell lung cancer with leptomeningeal metastasis (2026 edition)[J]. Chin J Lung Cancer, 2026, 29(1): 1-14. DOI: 10.3779/j.issn.1009-3419.2025.102.39.
[34]
HONG J J, HAO Y, YUAN J X, et al. Advances in the research of leptomeningeal metastases in non-small cell lung cancer: a narrative review[J]. Transl Lung Cancer Res, 2025, 14(8): 3216-3232. DOI: 10.21037/tlcr-2025-163.
[35]
OCANTO A, TORRES L, MONTIJANO M, et al. MR-LINAC, a new partner in radiation oncology: current landscape[J/OL]. Cancers, 2024, 16(2): 270 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/38254760/. DOI: 10.3390/cancers16020270.
[36]
GUCKENBERGER M, ANDRATSCHKE N, CHUNG C, et al. The future of MR-guided radiation therapy[J]. Semin Radiat Oncol, 2024, 34(1): 135-144. DOI: 10.1016/j.semradonc.2023.10.015.
[37]
KUMAR S, HOLLOWAY L, BOXER M, et al. Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for lung radiotherapy[J/OL]. Radiother Oncol, 2022, 167: 292-299 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/34896156/. DOI: 10.1016/j.radonc.2021.11.036.
[38]
ZHANG P X, DING S L, PENG K Q, et al. Comparing the outcomes of MR-based versus CT-based tumor delineation in locally advanced non-small cell lung cancer treated with hypo-fractionated radiotherapy and concurrent chemotherapy[J]. Transl Lung Cancer Res, 2024, 13(11): 2890-2902. DOI: 10.21037/tlcr-24-341.
[39]
REGNERY S, KATSIGIANNOPULOS E, HOEGEN P, et al. To fly or not to fly: Stereotactic MR-guided adaptive radiotherapy effectively treats ultracentral lung tumors with favorable long-term outcomes[J/OL]. Lung Cancer, 2023, 179: 107175 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/36965207/. DOI: 10.1016/j.lungcan.2023.03.011.
[40]
LA ROSA A, MITTAUER K E, BASSIRI N, et al. Accelerated hypofractionated magnetic resonance guided adaptive radiation therapy for ultracentral lung tumors[J]. Tomography, 2024, 10(1): 169-180. DOI: 10.3390/tomography10010013.
[41]
SRIDHARAN N, SALEM A, LITTLE R A, et al. Measuring repeatability of dynamic contrast-enhanced MRI biomarkers improves evaluation of biological response to radiotherapy in lung cancer[J]. Eur Radiol, 2025, 35(2): 664-673. DOI: 10.1007/s00330-024-10970-7.
[42]
LIU S Q, LV Y, DUAN L W, et al. Development and validation of multi-sequence magnetic resonance imaging radiomics models for predicting tumor response to radiotherapy in locally advanced non-small cell lung cancer[J]. J Thorac Dis, 2025, 17(3): 1684-1697. DOI: 10.21037/jtd-2025-142.
[43]
YU J H, TANG X F, LEI Y F, et al. A review on functional lung avoidance radiotherapy plan for lung cancer[J/OL]. Front Oncol, 2024, 14: 1429837 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/39703855/. DOI: 10.3389/fonc.2024.1429837.
[44]
GEFTER W B, LEE K S, SCHIEBLER M L, et al. Pulmonary functional imaging: part 2-state-of-the-art clinical applications and opportunities for improved patient care[J]. Radiology, 2021, 299(3): 524-538. DOI: 10.1148/radiol.2021204033.
[45]
STEWART N J, SMITH L J, CHAN H F, et al. Lung MRI with hyperpolarised gases: current & future clinical perspectives[J/OL]. Br J Radiol, 2022, 95(1132): 20210207 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/34106792/. DOI: 10.1259/bjr.20210207.
[46]
RANKINE L J, WANG Z Y, KELSEY C R, et al. Hyperpolarized 129Xe magnetic resonance imaging for functional avoidance treatment planning in thoracic radiation therapy: a comparison of ventilation- and gas exchange-guided treatment plans[J]. Int J Radiat Oncol Biol Phys, 2021, 111(4): 1044-1057. DOI: 10.1016/j.ijrobp.2021.07.002.
[47]
MIDRONI J, SALUNKHE R, LIU Z H, et al. Incorporation of functional lung imaging into radiation therapy planning in patients with lung cancer: a systematic review and meta-analysis[J]. Int J Radiat Oncol, 2024, 120(2): 370-408. DOI: 10.1016/j.ijrobp.2024.04.001.
[48]
YAREMKO B P, CAPALDI D P I, SHEIKH K, et al. Functional lung avoidance for individualized radiation therapy: results of a double-masked, randomized controlled trial[J]. Int J Radiat Oncol Biol Phys, 2022, 113(5): 1072-1084. DOI: 10.1016/j.ijrobp.2022.04.047.
[49]
KLAAR R, RABE M, STÜBER A T, et al. MRI-based ventilation and perfusion imaging to predict radiation-induced pneumonitis in lung tumor patients at a 0.35 T MR-Linac[J/OL]. Radiother Oncol, 2024, 199: 110468 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/39414083/. DOI: 10.1016/j.radonc.2024.110468.
[50]
FENG P Y, SHAO Z H, DONG B, et al. Application of diffusion kurtosis imaging and 18F-FDG PET in evaluating the subtype, stage and proliferation status of non-small cell lung cancer[J/OL]. Front Oncol, 2022, 12: 989131 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/36248958/. DOI: 10.3389/fonc.2022.989131.
[51]
HAN H Z, GUO W X, HAO H T, et al. Predictive value of quantitative parameters enhanced magnetic resonance imaging T 1 mapping for early chemotherapy efficacy in lung cancer[J]. Chin J Anat Clin, 2025, 30(2): 87-93. DOI: 10.3760/cma.j.cn101202-20240626-00203.
[52]
YUAN Z, NIU X M, LIU X M, et al. Use of diffusion-weighted magnetic resonance imaging (DW-MRI) to predict early response to anti-tumor therapy in advanced non-small cell lung cancer (NSCLC): a comparison of intravoxel incoherent motion-derived parameters and apparent diffusion coefficient[J]. Transl Lung Cancer Res, 2021, 10(8): 3671-3681. DOI: 10.21037/tlcr-21-610.
[53]
JIANG J Q, LENG Y, LIU J, et al. Volumetric analysis of intravoxel incoherent motion diffusion-weighted imaging for predicting the response to chemotherapy in patients with locally advanced non–small cell lung cancer[J]. J Comput Assist Tomogr, 2022, 46(3): 406-412. DOI: 10.1097/rct.0000000000001282.
[54]
ZHU J, YUN J, WANG K X, et al. Assessment of early response to lung cancer chemotherapy by semiquantitative analysis of dynamic contrast-enhanced MRI[J/OL]. Dis Markers, 2022, 2022: 2669281 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/35915736/. DOI: 10.1155/2022/2669281.
[55]
CHEN Y, WANG A K, LIANG F L, et al. Clinical application of DCE-MRI in evaluating the efficacy of BACE in lung cancer[J/OL]. Chin J Lung Dis Electron Ed, 2023, 16(6): 871-873 [2026-03-24]. https://zhfbjbzz.cma-cmc.com.cn/CN/10.3877/cma.j.issn.1674-6902.2023.06.029. DOI: 10.3877/cma.j.issn.1674-6902.2023.06.029.
[56]
LI J X, WU B L, HUANG Z, et al. Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions[J/OL]. Front Oncol, 2022, 12: 1082454 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/36741699/. DOI: 10.3389/fonc.2022.1082454.
[57]
ZHENG Y, LI J, CHEN K, et al. Comparison of conventional DWI, intravoxel incoherent motion imaging, and diffusion kurtosis imaging in differentiating lung lesions[J/OL]. Front Oncol, 2021, 11: 815967 [2026-03-24]. https://pubmed.ncbi.nlm.nih.gov/35127530/. DOI: 10.3389/fonc.2021.815967.

PREV Advances in CT and MRI for the assessment of progressive pulmonary fibrosis
NEXT Application progress of multi-parametric magnetic resonance imaging radiomics in the diagnosis and treatment of lung cancer
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn